Hi everyone,

Was wondering if anyone has interviewed with Peregrine Technologies and has any tips? Currently in the interview process and would like tips for each stage of the interview process.

They hire very high-caliber people and I would like to be prepared.

Happy to share more over DM.

Hi everyone,

I recently completed the CodeSignal OA for the BCG X AI Engineer role. From what I understand, the next step is a live coding interview that likely includes writing tests/handling edge cases during the session. (Please correct me if I'm wrong on this!)

For those who have gone through this process:

1. Focus & Topics: Are the live coding questions primarily Data Structures & Algorithms (DSA), or do they lean heavily toward AI/ML coding (e.g., NumPy/Pandas data manipulation, ML model implementation, custom data pipelines)?
2. Difficulty Level: What difficulty level should I prepare for (e.g., LeetCode Medium vs. Hard)?
3. Format: Is it a pure coding round, or does it blend coding with live test-case design/software engineering principles?

Any insights, tips, or guidance on how to best prepare for this stage would be greatly appreciated!

Thanks in advance!

Paid for Lodely expecting real interview prep and got what reads like pure AI-generated filler. They list brainteasers under Google, which has had a public no-brainteasers policy for over a decade, exactly the mistake you make when you’re generating plausible-looking content instead of sourcing it from actual candidates.

The “guides” are generic common-sense lines you could get free from any blog or just by asking an LLM yourself. Maybe someone else got value out of it, but for me it was money down the drain. Save it and use the free stuff that actually works.

Most "AI + IoT" implementations right now are just glorified chatbots—someone dumps sensor logs into a database, connects a basic RAG pipeline, and lets an operator ask, "What was the temperature in Zone 4 yesterday?"

In production environments dealing with autonomous drones, hundreds of ESP32 nodes, and Smart Building Management Systems (BMS), answering questions like a standard LLM isn't enough. You need a functional, closed-loop control system that ingests high-frequency telemetry, correlates cross-domain anomalies, checks engineering manuals, validates safety limits, and pushes actual actuation commands back down to the hardware.

Here is how we architected an end-to-end system bridging MQTT edge devices with Swarm Intelligence (SI), Medallion Stream Pipelines, Hybrid 3-Way RAG, and LangGraph Agentic workflows.

The Core Problem: You Can't Shove Raw MQTT into an LLM If you have drones streaming IMU/thermal data at 50Hz, ESP32s pushing vibration/gas metrics, and a building BMS sending BACnet/Modbus states over MQTT, you hit three walls immediately:

1. Latency: An LLM takes 1–3 seconds to reason. A drone avoiding an obstacle needs a decision in under 50ms.
2. Signal-to-Noise: Vector databases are terrible at interpreting raw JSON numbers like {"v_hz": 142.8, "temp": 88.4}.
3. Safety: You cannot let a hallucinating LLM publish an MQTT command that shuts down a primary chiller or crashes a drone.

To solve this, we split the architecture into a Fast Loop (Swarm Intelligence) and a Deep Loop (LangGraph + Hybrid RAG) connected via a Medallion streaming pipeline.

1. Edge & Nervous System: MQTT + Sparkplug B Everything starts and ends at the enterprise broker (EMQX / HiveMQ):

• Sources: IoT Drones (telemetry, LiDAR/thermal metadata), ESP32 microcontrollers (vibration, VOC, acoustic), and Smart Building BMS (HVAC, chillers, power relays).
• Topic Design & QoS:
  • enterprise/drones/{id}/telemetry -> QoS 0 (high-frequency stream)
  • enterprise/buildings/{zone}/esp32/alerts -> QoS 1 (threshold breaches)
  • enterprise/actuation/{target}/cmd -> QoS 2 (exactly-once closed-loop C2 actuation)
• Using Sparkplug B ensures state awareness (birth/death certificates and retained state) so downstream agents always know if a sensor is offline vs. idle.

2. The Data Pipeline: Medallion Streaming (Turning Numbers into Semantics) Before AI touches the data, a Kafka + PySpark/Flink pipeline processes the stream:

• Bronze: Raw MQTT topic ingestion for replay and audit.
• Silver: Stateful 5-minute sliding window aggregations, outlier detection, and joining raw device_ids with physical asset metadata.
• Gold (The Semantic Bridge): When an anomaly trips, the pipeline translates the numerical state into a structured semantic event for the agent core:"Alert [Zone-04 / AHU-02]: Vibration spiked to 142Hz (+3.4 SD) with bearing temp at 88.4°C. Concurrent ESP32-19 on Roof Sector 4 reports 38 km/h wind gusts."

3. Dual-Brain Intelligence: Swarm Intelligence (SI) vs. Agentic AI We separate reflexes from reasoning:

• Fast Loop (<50ms) — Swarm Intelligence (SI): Handles decentralized drone collision avoidance, local path re-routing, and immediate ESP32 failsafes. If an ESP32 detects a thermal spike on a roof sector, SI automatically tasks the nearest patrolling drone to fly over and stream thermal video—no LLM required.
• Deep Loop (1–3s) — LangGraph Multi-Agent Core: Handles complex root-cause analysis, cross-system orchestration, and safe actuation.

4. Hybrid 3-Way RAG (Why Vanilla RAG Fails for IoT) When the LangGraph Diagnostic Agent wakes up to investigate an anomaly, searching PDFs isn't enough. It runs three parallel queries:

1. Time-Series DB (TimescaleDB / InfluxDB): Pulls the last 30 minutes of telemetry curves to inspect the rate of change.
2. Knowledge Graph (Neo4j): Queries asset topology (ESP32-12 is mounted on AHU-02 -> which feeds Server Room 3 -> which has redundant unit AHU-03).
3. Vector DB (Qdrant / Milvus): Pulls the exact OEM maintenance manual, fault code tables, and Standard Operating Procedures (SOPs) for that specific equipment model.

5. LangGraph as a Functional State Machine (Not a Chatbot) Instead of a single prompt, the cognitive layer is a stateful graph in LangGraph:

• Supervisor Node: Triages incoming Gold-layer anomaly events and initializes graph state.
• Diagnostic Node: Executes the 3-way Hybrid RAG lookup and formulates a remediation plan ("Bearing seizure imminent on AHU-02. Spin up standby AHU-03 and latch AHU-02 off").
• Deterministic Safety & Guardrail Node: Pure deterministic rule validation—not LLM vibes. It checks physical interlocks: Will spinning up AHU-03 exceed current electrical grid load? Does Drone-02 have >30% battery reserve? If it fails, it loops back to re-plan or escalates to a human operator.
• Actuator Tool Node: Once interlocks pass, the agent calls a tool that publishes a structured payload back to the MQTT broker on QoS 2 to execute the hardware state change, waits for the Sparkplug B ACK, and auto-generates an SLA maintenance ticket with the drone's thermal snapshot attached.

TL;DR:

• MQTT = The nervous system (transport + state).
• Stream Pipeline (Kafka/PySpark) = The sensory cortex (turning raw numbers into semantic events).
• Swarm Intelligence (SI) = The spinal cord (sub-50ms reflexes and drone coordination).
• Hybrid RAG (TSDB + Graph + Vector) = Institutional & spatial memory.
• LangGraph = The deliberative brain with deterministic safety interlocks that actually executes commands rather than just chatting.

Curious how others here are handling deterministic guardrails when letting agentic workflows trigger physical actuators or MQTT command topics. Are you keeping a strict Human-in-the-Loop (HITL) approval step for QoS 2 writes, or using bounded rule engines in front of the broker?

24M, it has been 2 years that i have been working in this Machine Learning field, worked in LLM fine-tuning, computer vision projects yet, i think i got everything, 2025 CS grad, currently doing an Mphil. in artificial Intelligence too

Needed some help if someone can hook me up in some remote role, a junior or mid level will also work since i think i got the right experience for this field, would mean a lot to me

please comment, willing to drop resume and connect asap :)

Hey everyone!

I recently completed my first interview for the Deployment Strategist role at Palantir. I haven't heard back from the recruiter yet, but my candidate portal has updated to "Mid Stage Interviews" and says I've made it to the next step.

I don't know which interviews I'll be doing next, but I'm trying to prepare for both Decomposition and Technical Comprehension.

For anyone who's been through the DS process recently:

• What were the interviews actually like, beyond what's explained in the official prep guides?
• How did you prepare, and what resources or exercises helped you the most?
• Looking back, what would you have done differently?

Any insights or advice would be massively appreciated. Thanks!

I have an upcoming interview with Woven by Toyota for an MLE perception role. I was told the round would be focused on ML coding/debugging. Specifically, I was told to focus on Python fundamentals, PyTorch, tensor operations and dimensions/shapes, common model-training code patterns, and debugging ML code.

Here are some common ways I've studied:

• Asking Claude to walk me through transformer, ViT, and CNN architectures (a basic one and U-Net) for my preparation. I would trace the shapes all the way through.
• Getting familiar with broadcasting rules (start from the left, see if the dimensions are equal, or are 1, and if so, take the greater one to get the resulting shape)
• Slicing tensors (i.e., how to get a column, how to get a row, etc.)
• Understanding why models can fail silently -- suspicious training vs val results, getting NaN for validation
• What a PyTorch loop looks like
• Common Python errors like indexing errors or value errors; iterators, generators, etc.

What are other suggestions? What architecture do they actually ask you about in this interview? Any help would be appreciated.

Getting an offer of 45lpa all cash with yearly bonus of 10lpa. Current TC is 50lpa(30 cash + 20 rsus per year). On paper I am getting more fixed and comp is moving towards cash heavy, but the overall hike on TC is 10%.

Right now the comp is heavily dependent on stocks. If they tank the TC drops. But there is upside of stock price rising too which can easily cover the hike I am getting

Yoe: 4 years

Any suggestions, recommendations, guidance on applying to deployment strategist? I am desperate to get this job. Any advice is welcomed.

Summary:

Do software engineers belong on the endangered species list? You'd think so, based on the headlines from the last couple of years.

Yet, my day-to-day experience is the exact opposite: I know more people today writing code and building products than I've ever known before. They're using AI, of course, but they're creating software for the first time in their lives, when they previously didn't have the time or confidence to even try.

Here are some quick examples:

- My brother, a talented product designer, is now building his own iOS apps.
- My 66-year-old mother-in-law is writing Python scripts and SQL queries at work.
- My cofounder, a product manager, started creating PRs in our codebase again.
- My friend in Brooklyn built a home automation system for his newborn's nursery.

In other words, virtually everyone I know has become a de facto engineer, or has at least started to code in their spare time.

These stories are anecdotal, but they're not special cases. Check out the metrics of most developer platforms and you'll see they're off the charts: GitHub has experienced accelerating growth with US developers up 25% year-over-year. Hosting platforms like Vercel, Supabase, Render, and Replit have likewise surged in adoption, as people clamor to ship projects at unprecedented velocity.

Official employment surveys, like those published by IPUMS, suggest that the number of professional software developers in the US is not on the decline, but rather continues to grow and even reached an all-time high in 2026.

Even the story at universities doesn't seem as negative as you'd expect. Princeton publishes detailed statistics on student majors every year. Computer Science remained the #1 most popular major among upperclassmen by a healthy margin in 2025-2026. Engineering as a whole continues to grow, particularly electrical engineering departments.

I think when people talk about the end of software engineering, what they really want to talk about is its sudden democratization and ubiquity. I'm struggling to decide if this is even the contradiction that I'm making it out to be, or simply two sides of the same coin.

The triple threat of AI automation, oversupply of CS majors, and lower barrier to entry might mean that software engineering is simultaneously more popular than ever and doomed.

Full post here: https://www.jacob.co/blog/everyone-is-an-engineer

Hi guys!

I have an upcoming interview at A16z (Andreessen Horowitz) in the Bay Area for a DevX Software Engineer (Partner 16, Partner 18), ASG role. Has anybody recently interviewed at A16z? Curious to hear y'all's experience:

- What parts of your interview did you find difficult?

- Anything unexpected show up during your parts?

- Did you have a "security" round? I have one upcoming, but security is a broad topic, and I particularly need help with this.

Has anyone done a round 2 at Palantir for the Deployment Strategist Role? The decomposition round will they ask me to write SQL?

For Palantir deployment strategist or palantir in general has anyone ever talked to more than one hiring manager, if so what does that mean?

Hi, London-based strategy consultant, 6+ YOE, currently interviewing for experienced-hire strategy/AI/tech roles. Looking for another working professional (ideally 5–10 YOE) to do weekly mock interviews/cases. Let me know :)

Wondering if people who are actively interviewing with tech companies for the tech jobs are using any kind of tools for notes taking so you can review how you performed, what you answered and where you may need to improve. How has been your experience as candidates and how do you ask for permissions of you decide to use such tools that may record the conversations. The companies usually record this and they take notes and tell you they are doing that, but I wonder what has been your experience if you asked if you could record the conversations for your own notes taking.